Author: mskotiniotis

The theory of open quantum system is one of the most essential tools for the development of quantum technologies. Furthermore, the Lindblad (or Gorini-Kossakowski-Sudarshan-Lindblad) Master Equation plays a key role as it is the most general generator of Markovian dynamics in quantum systems. In this presentation, we present this equation together with its derivation from microscopic principles. The presentation tries to be as self-contained and straightforward as possible to be useful to readers with no previous knowledge of this field. Undergrad, master, and PhD students are encouraged to assist.

Reference: https://pubs.aip.org/aip/adv/article/10/2/025106/1021638/A-short-introduction-to-the-Lindblad-master

Link to video

https://gloton.ugr.es/owncloud/index.php/s/pKaLRyWuVD7Ewae

The theory of open quantum system is one of the most essential tools for the development of quantum technologies. Furthermore, the Lindblad (or Gorini-Kossakowski-Sudarshan-Lindblad) Master Equation plays a key role as it is the most general generator of Markovian dynamics in quantum systems. In this presentation, we present this equation together with its derivation from microscopic principles. The presentation tries to be as self-contained and straightforward as possible to be useful to readers with no previous knowledge of this field. Undergrad, master, and PhD students are encouraged to assist.

Reference: https://pubs.aip.org/aip/adv/article/10/2/025106/1021638/A-short-introduction-to-the-Lindblad-master

Link to google meet
https://meet.google.com/boc-uamd-iti

Link to video of first lecture

https://gloton.ugr.es/owncloud/index.php/s/73FkMiOen2Ggrfb

Quantum computing is an emerging research field at the intersection of physics, computer science and applied mathematics. Quantum computers are devices that exploit coherent quantum phenomena to speed-up calculations. Quantum machine learning is a subfield of quantum computing that investigates the application of quantum computers algorithms for machine learning problems. This course will give an elementary introduction to quantum machine learning, and review a few examples of quantum algorithms for classification and clustering problems. In our first meeting, we will give a general introduction, and review quantum algorithms for solving linear systems of equations, Grover search and simulating sparse Hamiltonian dynamics.

Date: 16 June 2023

Time: 11:00 am

Location: Computational Physics Laboratory, Department of Electromagnetism and Condensed Mater, Ground Floor, Faculty of Science (next to the giant pendulum)

Quantum computing is an emerging research field at the intersection of physics, computer science and applied mathematics. Quantum computers are devices that exploit coherent quantum phenomena to speed-up calculations. Quantum machine learning is a subfield of quantum computing that investigates the application of quantum computers algorithms for machine learning problems. This course will give an elementary introduction to quantum machine learning, and review a few examples of quantum algorithms for classification and clustering problems. In our first meeting, we will give a general introduction, and review quantum algorithms for solving linear systems of equations, Grover search and simulating sparse Hamiltonian dynamics.

Date: 13 June 2023

Time: 11:00 am

Location: Computational Physics Laboratory, Department of Electromagnetism and Condensed Mater, Ground Floor, Faculty of Science (next to the giant pendulum)

Quantum computing is an emerging research field at the intersection of physics, computer science and applied mathematics. Quantum computers are devices that exploit coherent quantum phenomena to speed-up calculations. Quantum machine learning is a subfield of quantum computing that investigates the application of quantum computers algorithms for machine learning problems. This course will give an elementary introduction to quantum machine learning, and review a few examples of quantum algorithms for classification and clustering problems. In our first meeting, we will give a general introduction, and review quantum algorithms for solving linear systems of equations, Grover search and simulating sparse Hamiltonian dynamics.

Date: 2 June 2023

Time: 11:00 am

Location: Computational Physics Laboratory, Department of Electromagnetism and Condensed Mater,  Ground Floor, Faculty of Science (next to the giant pendulum)